from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 13.0 | 19.660085 |
| daal4py_KNeighborsClassifier | 0.0 | 5.0 | 10.622561 |
| KNeighborsClassifier_kd_tree | 0.0 | 5.0 | 52.867885 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 1.0 | 45.414334 |
| KMeans_tall | 0.0 | 1.0 | 45.612841 |
| daal4py_KMeans_tall | 0.0 | 1.0 | 18.235142 |
| KMeans_short | 0.0 | 0.0 | 23.888986 |
| daal4py_KMeans_short | 0.0 | 0.0 | 13.454837 |
| LogisticRegression | 0.0 | 1.0 | 7.549737 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 59.564112 |
| Ridge | 0.0 | 0.0 | 27.080876 |
| daal4py_Ridge | 0.0 | 0.0 | 8.942399 |
| total | 0.0 | 32.0 | 32.975178 |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | n_jobs | n_neighbors | throughput | latency | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 0.130 | 0.003 | 1000000 | 1000000 | 100 | -1 | 1 | 6.174 | NaN | 0.985 | 0.983 | 0.495 | 0.013 | 0.262 | 0.010 | See |
| 1 | KNeighborsClassifier | predict | 31.838 | 0.000 | 1000000 | 1000 | 100 | -1 | 1 | 0.000 | 0.032 | 0.985 | 0.983 | 3.773 | 0.037 | 8.438 | 0.083 | See |
| 2 | KNeighborsClassifier | predict | 0.189 | 0.011 | 1000000 | 1 | 100 | -1 | 1 | 0.004 | 0.000 | 0.985 | 0.983 | 0.103 | 0.002 | 1.834 | 0.116 | See |
| 3 | KNeighborsClassifier | fit | 0.126 | 0.005 | 1000000 | 1000000 | 100 | -1 | 5 | 6.359 | NaN | 0.985 | 0.983 | 0.504 | 0.015 | 0.249 | 0.012 | See |
| 4 | KNeighborsClassifier | predict | 36.199 | 0.000 | 1000000 | 1000 | 100 | -1 | 5 | 0.000 | 0.036 | 0.985 | 0.983 | 3.786 | 0.019 | 9.561 | 0.049 | See |
| 5 | KNeighborsClassifier | predict | 0.209 | 0.010 | 1000000 | 1 | 100 | -1 | 5 | 0.004 | 0.000 | 0.985 | 0.983 | 0.108 | 0.004 | 1.932 | 0.117 | See |
| 6 | KNeighborsClassifier | fit | 0.131 | 0.003 | 1000000 | 1000000 | 100 | -1 | 100 | 6.129 | NaN | 0.985 | 0.983 | 0.503 | 0.010 | 0.260 | 0.009 | See |
| 7 | KNeighborsClassifier | predict | 36.197 | 0.000 | 1000000 | 1000 | 100 | -1 | 100 | 0.000 | 0.036 | 0.985 | 0.983 | 3.845 | 0.023 | 9.415 | 0.056 | See |
| 8 | KNeighborsClassifier | predict | 0.211 | 0.017 | 1000000 | 1 | 100 | -1 | 100 | 0.004 | 0.000 | 0.985 | 0.983 | 0.102 | 0.002 | 2.077 | 0.170 | See |
| 9 | KNeighborsClassifier | fit | 0.141 | 0.004 | 1000000 | 1000000 | 100 | 1 | 1 | 5.693 | NaN | 0.985 | 0.983 | 0.512 | 0.010 | 0.274 | 0.009 | See |
| 10 | KNeighborsClassifier | predict | 15.448 | 0.044 | 1000000 | 1000 | 100 | 1 | 1 | 0.000 | 0.015 | 0.985 | 0.983 | 3.786 | 0.026 | 4.080 | 0.031 | See |
| 11 | KNeighborsClassifier | predict | 0.209 | 0.009 | 1000000 | 1 | 100 | 1 | 1 | 0.004 | 0.000 | 0.985 | 0.983 | 0.106 | 0.003 | 1.963 | 0.096 | See |
| 12 | KNeighborsClassifier | fit | 0.130 | 0.005 | 1000000 | 1000000 | 100 | 1 | 5 | 6.143 | NaN | 0.985 | 0.983 | 0.511 | 0.014 | 0.255 | 0.012 | See |
| 13 | KNeighborsClassifier | predict | 22.061 | 0.034 | 1000000 | 1000 | 100 | 1 | 5 | 0.000 | 0.022 | 0.985 | 0.983 | 3.774 | 0.015 | 5.845 | 0.026 | See |
| 14 | KNeighborsClassifier | predict | 0.224 | 0.009 | 1000000 | 1 | 100 | 1 | 5 | 0.004 | 0.000 | 0.985 | 0.983 | 0.106 | 0.003 | 2.113 | 0.100 | See |
| 15 | KNeighborsClassifier | fit | 0.125 | 0.004 | 1000000 | 1000000 | 100 | 1 | 100 | 6.381 | NaN | 0.985 | 0.983 | 0.501 | 0.014 | 0.250 | 0.011 | See |
| 16 | KNeighborsClassifier | predict | 22.182 | 0.037 | 1000000 | 1000 | 100 | 1 | 100 | 0.000 | 0.022 | 0.985 | 0.983 | 3.823 | 0.021 | 5.803 | 0.033 | See |
| 17 | KNeighborsClassifier | predict | 0.220 | 0.008 | 1000000 | 1 | 100 | 1 | 100 | 0.004 | 0.000 | 0.985 | 0.983 | 0.106 | 0.002 | 2.064 | 0.086 | See |
| 18 | KNeighborsClassifier | fit | 0.055 | 0.001 | 1000000 | 1000000 | 2 | -1 | 1 | 0.292 | NaN | 0.985 | 0.983 | 0.095 | 0.003 | 0.579 | 0.026 | See |
| 19 | KNeighborsClassifier | predict | 25.081 | 0.190 | 1000000 | 1000 | 2 | -1 | 1 | 0.000 | 0.025 | 0.985 | 0.983 | 0.794 | 0.022 | 31.580 | 0.923 | See |
| 20 | KNeighborsClassifier | predict | 0.021 | 0.001 | 1000000 | 1 | 2 | -1 | 1 | 0.001 | 0.000 | 0.985 | 0.983 | 0.004 | 0.001 | 4.801 | 0.682 | See |
| 21 | KNeighborsClassifier | fit | 0.053 | 0.001 | 1000000 | 1000000 | 2 | -1 | 5 | 0.300 | NaN | 0.985 | 0.983 | 0.094 | 0.004 | 0.565 | 0.028 | See |
| 22 | KNeighborsClassifier | predict | 32.716 | 0.000 | 1000000 | 1000 | 2 | -1 | 5 | 0.000 | 0.033 | 0.985 | 0.983 | 0.791 | 0.014 | 41.357 | 0.707 | See |
| 23 | KNeighborsClassifier | predict | 0.032 | 0.002 | 1000000 | 1 | 2 | -1 | 5 | 0.001 | 0.000 | 0.985 | 0.983 | 0.004 | 0.000 | 7.062 | 0.732 | See |
| 24 | KNeighborsClassifier | fit | 0.054 | 0.002 | 1000000 | 1000000 | 2 | -1 | 100 | 0.299 | NaN | 0.985 | 0.983 | 0.096 | 0.002 | 0.556 | 0.024 | See |
| 25 | KNeighborsClassifier | predict | 32.336 | 0.000 | 1000000 | 1000 | 2 | -1 | 100 | 0.000 | 0.032 | 0.985 | 0.983 | 0.870 | 0.023 | 37.181 | 0.970 | See |
| 26 | KNeighborsClassifier | predict | 0.032 | 0.002 | 1000000 | 1 | 2 | -1 | 100 | 0.001 | 0.000 | 0.985 | 0.983 | 0.004 | 0.001 | 7.162 | 1.088 | See |
| 27 | KNeighborsClassifier | fit | 0.053 | 0.001 | 1000000 | 1000000 | 2 | 1 | 1 | 0.301 | NaN | 0.985 | 0.983 | 0.098 | 0.005 | 0.540 | 0.030 | See |
| 28 | KNeighborsClassifier | predict | 10.519 | 0.210 | 1000000 | 1000 | 2 | 1 | 1 | 0.000 | 0.011 | 0.985 | 0.983 | 0.792 | 0.015 | 13.280 | 0.368 | See |
| 29 | KNeighborsClassifier | predict | 0.015 | 0.002 | 1000000 | 1 | 2 | 1 | 1 | 0.001 | 0.000 | 0.985 | 0.983 | 0.004 | 0.001 | 3.542 | 0.640 | See |
| 30 | KNeighborsClassifier | fit | 0.054 | 0.001 | 1000000 | 1000000 | 2 | 1 | 5 | 0.298 | NaN | 0.985 | 0.983 | 0.094 | 0.002 | 0.574 | 0.018 | See |
| 31 | KNeighborsClassifier | predict | 18.395 | 0.047 | 1000000 | 1000 | 2 | 1 | 5 | 0.000 | 0.018 | 0.985 | 0.983 | 0.787 | 0.011 | 23.381 | 0.342 | See |
| 32 | KNeighborsClassifier | predict | 0.027 | 0.002 | 1000000 | 1 | 2 | 1 | 5 | 0.001 | 0.000 | 0.985 | 0.983 | 0.004 | 0.000 | 6.560 | 0.887 | See |
| 33 | KNeighborsClassifier | fit | 0.053 | 0.001 | 1000000 | 1000000 | 2 | 1 | 100 | 0.300 | NaN | 0.985 | 0.983 | 0.094 | 0.004 | 0.565 | 0.029 | See |
| 34 | KNeighborsClassifier | predict | 18.370 | 0.070 | 1000000 | 1000 | 2 | 1 | 100 | 0.000 | 0.018 | 0.985 | 0.983 | 0.851 | 0.017 | 21.589 | 0.431 | See |
| 35 | KNeighborsClassifier | predict | 0.026 | 0.002 | 1000000 | 1 | 2 | 1 | 100 | 0.001 | 0.000 | 0.985 | 0.983 | 0.006 | 0.001 | 4.317 | 1.012 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | n_jobs | n_neighbors | throughput | latency | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 2.694 | 0.041 | 1000000 | 1000000 | 10 | -1 | 1 | 0.030 | NaN | 0.99 | 0.984 | 0.696 | 0.019 | 3.872 | 0.120 | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 0.450 | 0.014 | 1000000 | 1000 | 10 | -1 | 1 | 0.000 | 0.000 | 0.99 | 0.984 | 0.122 | 0.005 | 3.681 | 0.183 | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.000 | 1000000 | 1 | 10 | -1 | 1 | 0.026 | 0.000 | 0.99 | 0.984 | 0.000 | 0.000 | 12.388 | 5.145 | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 2.699 | 0.034 | 1000000 | 1000000 | 10 | -1 | 5 | 0.030 | NaN | 0.99 | 0.984 | 0.757 | 0.012 | 3.564 | 0.073 | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 0.874 | 0.017 | 1000000 | 1000 | 10 | -1 | 5 | 0.000 | 0.001 | 0.99 | 0.984 | 0.213 | 0.004 | 4.098 | 0.109 | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 0.004 | 0.001 | 1000000 | 1 | 10 | -1 | 5 | 0.023 | 0.000 | 0.99 | 0.984 | 0.000 | 0.000 | 7.321 | 2.937 | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 2.714 | 0.036 | 1000000 | 1000000 | 10 | -1 | 100 | 0.029 | NaN | 0.99 | 0.984 | 0.690 | 0.019 | 3.931 | 0.121 | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 2.872 | 0.034 | 1000000 | 1000 | 10 | -1 | 100 | 0.000 | 0.003 | 0.99 | 0.984 | 0.688 | 0.014 | 4.177 | 0.099 | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 0.005 | 0.001 | 1000000 | 1 | 10 | -1 | 100 | 0.015 | 0.000 | 0.99 | 0.984 | 0.001 | 0.000 | 6.002 | 2.892 | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 2.726 | 0.063 | 1000000 | 1000000 | 10 | 1 | 1 | 0.029 | NaN | 0.99 | 0.984 | 0.738 | 0.017 | 3.693 | 0.122 | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 0.753 | 0.020 | 1000000 | 1000 | 10 | 1 | 1 | 0.000 | 0.001 | 0.99 | 0.984 | 0.122 | 0.006 | 6.148 | 0.344 | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 10 | 1 | 1 | 0.065 | 0.000 | 0.99 | 0.984 | 0.000 | 0.000 | 5.337 | 2.771 | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 2.690 | 0.038 | 1000000 | 1000000 | 10 | 1 | 5 | 0.030 | NaN | 0.99 | 0.984 | 0.687 | 0.013 | 3.914 | 0.093 | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1.458 | 0.027 | 1000000 | 1000 | 10 | 1 | 5 | 0.000 | 0.001 | 0.99 | 0.984 | 0.228 | 0.004 | 6.410 | 0.163 | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 0.002 | 0.000 | 1000000 | 1 | 10 | 1 | 5 | 0.047 | 0.000 | 0.99 | 0.984 | 0.000 | 0.000 | 3.907 | 1.846 | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 2.735 | 0.058 | 1000000 | 1000000 | 10 | 1 | 100 | 0.029 | NaN | 0.99 | 0.984 | 0.733 | 0.015 | 3.730 | 0.110 | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 4.798 | 0.074 | 1000000 | 1000 | 10 | 1 | 100 | 0.000 | 0.005 | 0.99 | 0.984 | 0.697 | 0.023 | 6.880 | 0.254 | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 0.004 | 0.001 | 1000000 | 1 | 10 | 1 | 100 | 0.022 | 0.000 | 0.99 | 0.984 | 0.001 | 0.000 | 4.121 | 2.176 | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 0.741 | 0.023 | 1000000 | 1000000 | 2 | -1 | 1 | 0.022 | NaN | 0.99 | 0.984 | 0.475 | 0.011 | 1.561 | 0.061 | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 0.032 | 0.002 | 1000000 | 1000 | 2 | -1 | 1 | 0.001 | 0.000 | 0.99 | 0.984 | 0.001 | 0.000 | 40.871 | 11.693 | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 0.009 | 0.020 | 1000000 | 1 | 2 | -1 | 1 | 0.002 | 0.000 | 0.99 | 0.984 | 0.000 | 0.000 | 59.130 | 131.071 | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 0.729 | 0.015 | 1000000 | 1000000 | 2 | -1 | 5 | 0.022 | NaN | 0.99 | 0.984 | 0.476 | 0.009 | 1.530 | 0.044 | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 0.037 | 0.010 | 1000000 | 1000 | 2 | -1 | 5 | 0.000 | 0.000 | 0.99 | 0.984 | 0.001 | 0.000 | 32.002 | 13.976 | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.001 | 1000000 | 1 | 2 | -1 | 5 | 0.006 | 0.000 | 0.99 | 0.984 | 0.000 | 0.000 | 18.176 | 10.967 | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 0.727 | 0.011 | 1000000 | 1000000 | 2 | -1 | 100 | 0.022 | NaN | 0.99 | 0.984 | 0.477 | 0.017 | 1.524 | 0.058 | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 0.050 | 0.003 | 1000000 | 1000 | 2 | -1 | 100 | 0.000 | 0.000 | 0.99 | 0.984 | 0.007 | 0.001 | 7.004 | 0.809 | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 0.003 | 0.000 | 1000000 | 1 | 2 | -1 | 100 | 0.006 | 0.000 | 0.99 | 0.984 | 0.000 | 0.000 | 11.521 | 7.471 | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 0.726 | 0.014 | 1000000 | 1000000 | 2 | 1 | 1 | 0.022 | NaN | 0.99 | 0.984 | 0.476 | 0.016 | 1.524 | 0.059 | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 0.029 | 0.002 | 1000000 | 1000 | 2 | 1 | 1 | 0.001 | 0.000 | 0.99 | 0.984 | 0.001 | 0.000 | 35.723 | 11.396 | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 1 | 0.020 | 0.000 | 0.99 | 0.984 | 0.000 | 0.000 | 4.829 | 2.801 | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 0.725 | 0.014 | 1000000 | 1000000 | 2 | 1 | 5 | 0.022 | NaN | 0.99 | 0.984 | 0.478 | 0.010 | 1.516 | 0.043 | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 0.032 | 0.001 | 1000000 | 1000 | 2 | 1 | 5 | 0.001 | 0.000 | 0.99 | 0.984 | 0.001 | 0.000 | 25.985 | 8.684 | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 5 | 0.019 | 0.000 | 0.99 | 0.984 | 0.000 | 0.000 | 4.886 | 2.572 | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 0.717 | 0.013 | 1000000 | 1000000 | 2 | 1 | 100 | 0.022 | NaN | 0.99 | 0.984 | 0.476 | 0.010 | 1.507 | 0.042 | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 0.056 | 0.004 | 1000000 | 1000 | 2 | 1 | 100 | 0.000 | 0.000 | 0.99 | 0.984 | 0.007 | 0.001 | 7.935 | 0.958 | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | 1 | 100 | 0.020 | 0.000 | 0.99 | 0.984 | 0.000 | 0.000 | 4.570 | 2.371 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | init | n_iter_sklearn | throughput | latency | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 0.588 | 0.019 | 1000000 | 1000000 | 2 | k-means++ | 30 | 0.816 | NaN | 0.002 | 30 | 0.003 | 0.304 | 0.010 | 1.932 | 0.091 | See |
| 1 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 2 | k-means++ | 30 | 0.010 | 0.0 | 0.002 | 30 | 0.003 | 0.000 | 0.000 | 7.276 | 3.651 | See |
| 2 | KMeans_tall | predict | 0.001 | 0.000 | 1000000 | 1 | 2 | k-means++ | 30 | 0.011 | 0.0 | 0.002 | 30 | 0.003 | 0.000 | 0.000 | 8.823 | 4.298 | See |
| 3 | KMeans_tall | fit | 0.502 | 0.013 | 1000000 | 1000000 | 2 | random | 30 | 0.957 | NaN | 0.002 | 30 | 0.003 | 0.260 | 0.013 | 1.926 | 0.106 | See |
| 4 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 2 | random | 30 | 0.010 | 0.0 | 0.002 | 30 | 0.003 | 0.000 | 0.000 | 7.837 | 3.651 | See |
| 5 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1 | 2 | random | 30 | 0.011 | 0.0 | 0.002 | 30 | 0.003 | 0.000 | 0.000 | 7.707 | 3.547 | See |
| 6 | KMeans_tall | fit | 7.131 | 0.129 | 1000000 | 1000000 | 100 | k-means++ | 30 | 3.365 | NaN | 0.002 | 30 | 0.003 | 3.947 | 0.066 | 1.807 | 0.045 | See |
| 7 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 100 | k-means++ | 30 | 0.466 | 0.0 | 0.002 | 30 | 0.003 | 0.000 | 0.000 | 5.921 | 2.384 | See |
| 8 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1 | 100 | k-means++ | 30 | 0.498 | 0.0 | 0.002 | 30 | 0.003 | 0.000 | 0.000 | 9.217 | 4.943 | See |
| 9 | KMeans_tall | fit | 6.617 | 0.049 | 1000000 | 1000000 | 100 | random | 30 | 3.627 | NaN | 0.002 | 30 | 0.003 | 3.788 | 0.056 | 1.747 | 0.029 | See |
| 10 | KMeans_tall | predict | 0.002 | 0.000 | 1000000 | 1000 | 100 | random | 30 | 0.424 | 0.0 | 0.002 | 30 | 0.003 | 0.000 | 0.000 | 6.581 | 2.810 | See |
| 11 | KMeans_tall | predict | 0.002 | 0.001 | 1000000 | 1 | 100 | random | 30 | 0.471 | 0.0 | 0.002 | 30 | 0.003 | 0.000 | 0.000 | 9.667 | 5.285 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | init | n_iter_sklearn | throughput | latency | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 0.314 | 0.014 | 10000 | 10000 | 2 | k-means++ | 30 | 0.015 | NaN | 0.005 | 30 | 0.003 | 0.147 | 0.004 | 2.132 | 0.110 | See |
| 1 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1000 | 2 | k-means++ | 30 | 0.008 | 0.0 | 0.005 | 30 | 0.003 | 0.001 | 0.000 | 2.835 | 0.624 | See |
| 2 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1 | 2 | k-means++ | 30 | 0.009 | 0.0 | 0.005 | 30 | 0.003 | 0.000 | 0.000 | 9.414 | 4.436 | See |
| 3 | KMeans_short | fit | 0.134 | 0.005 | 10000 | 10000 | 2 | random | 30 | 0.036 | NaN | 0.005 | 30 | 0.003 | 0.068 | 0.002 | 1.967 | 0.103 | See |
| 4 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1000 | 2 | random | 30 | 0.008 | 0.0 | 0.005 | 30 | 0.003 | 0.001 | 0.000 | 2.980 | 0.678 | See |
| 5 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1 | 2 | random | 30 | 0.011 | 0.0 | 0.005 | 30 | 0.003 | 0.000 | 0.000 | 8.054 | 3.852 | See |
| 6 | KMeans_short | fit | 1.042 | 0.040 | 10000 | 10000 | 100 | k-means++ | 16 | 0.123 | NaN | 0.005 | 23 | 0.003 | 0.578 | 0.032 | 1.802 | 0.121 | See |
| 7 | KMeans_short | predict | 0.003 | 0.000 | 10000 | 1000 | 100 | k-means++ | 16 | 0.241 | 0.0 | 0.005 | 23 | 0.003 | 0.001 | 0.000 | 2.248 | 0.466 | See |
| 8 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1 | 100 | k-means++ | 16 | 0.524 | 0.0 | 0.005 | 23 | 0.003 | 0.000 | 0.000 | 7.464 | 3.320 | See |
| 9 | KMeans_short | fit | 0.370 | 0.051 | 10000 | 10000 | 100 | random | 24 | 0.519 | NaN | 0.005 | 23 | 0.003 | 0.318 | 0.036 | 1.166 | 0.209 | See |
| 10 | KMeans_short | predict | 0.003 | 0.000 | 10000 | 1000 | 100 | random | 24 | 0.269 | 0.0 | 0.005 | 23 | 0.003 | 0.001 | 0.000 | 2.003 | 0.385 | See |
| 11 | KMeans_short | predict | 0.002 | 0.000 | 10000 | 1 | 100 | random | 24 | 0.477 | 0.0 | 0.005 | 23 | 0.003 | 0.000 | 0.000 | 7.656 | 3.566 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | class_weight | l1_ratio | n_jobs | random_state | n_iter | throughput | latency | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 15.053 | 0.084 | 1000000 | 1000000 | 100 | NaN | NaN | NaN | NaN | [20] | [-0.07838136] | NaN | 0.27 | 15.068 | 0.058 | 0.999 | 0.007 | See |
| 1 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | NaN | NaN | NaN | NaN | [20] | 2.383341397078287 | 0.0 | 0.27 | 0.000 | 0.000 | 0.705 | 0.283 | See |
| 2 | LogisticRegression | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | NaN | NaN | NaN | NaN | [20] | 7.905607056147397 | 0.0 | 0.27 | 0.000 | 0.000 | 0.376 | 0.262 | See |
| 3 | LogisticRegression | fit | 1.145 | 0.019 | 1000 | 1000 | 10000 | NaN | NaN | NaN | NaN | [26] | [1.81630515] | NaN | 0.27 | 1.088 | 0.017 | 1.053 | 0.024 | See |
| 4 | LogisticRegression | predict | 0.002 | 0.000 | 1000 | 100 | 10000 | NaN | NaN | NaN | NaN | [26] | 3.4045849118490943 | 0.0 | 0.27 | 0.004 | 0.000 | 0.614 | 0.097 | See |
| 5 | LogisticRegression | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | NaN | NaN | NaN | NaN | [26] | 58.77801964677965 | 0.0 | 0.27 | 0.001 | 0.000 | 0.166 | 0.131 | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | mean_sklearn | stdev_sklearn | n_samples_train | n_samples | n_features | max_iter | random_state | throughput | latency | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 0.277 | 0.005 | 1000 | 1000 | 10000 | NaN | NaN | 0.288 | NaN | 1.0 | 0.285 | 0.003 | 0.973 | 0.019 | See |
| 1 | Ridge | predict | 0.012 | 0.000 | 1000 | 1000 | 10000 | NaN | NaN | 6.699 | 0.0 | 1.0 | 0.021 | 0.001 | 0.581 | 0.027 | See |
| 2 | Ridge | predict | 0.000 | 0.000 | 1000 | 1 | 10000 | NaN | NaN | 715.025 | 0.0 | 1.0 | 0.000 | 0.000 | 0.616 | 0.487 | See |
| 3 | Ridge | fit | 1.244 | 0.033 | 1000000 | 1000000 | 100 | NaN | NaN | 0.643 | NaN | 1.0 | 0.339 | 0.010 | 3.666 | 0.149 | See |
| 4 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1000 | 100 | NaN | NaN | 4.823 | 0.0 | 1.0 | 0.000 | 0.000 | 0.657 | 0.374 | See |
| 5 | Ridge | predict | 0.000 | 0.000 | 1000000 | 1 | 100 | NaN | NaN | 9.002 | 0.0 | 1.0 | 0.000 | 0.000 | 0.659 | 0.528 | See |
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"system_info": {
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